As the proliferation of distributed storage systems continues to increase, so does the complexity of managing the infrastructure components comprising the systems. Specifically, an IT administrator is often tasked with not only managing the infrastructure currently deployed at managed sites, but also an IT administrator is often tasked with scaling the infrastructure to satisfy the forthcoming demand for compute and/or storage capacity. For example, the administrator might be responsible for cluster management (e.g., deployment, maintenance, scaling, etc.), virtual machine (VM) management (e.g., creation, placement, protection, migration, etc.), storage management (e.g., allocation, policy compliance, location, etc.), and/or management of other aspects of the infrastructure. In some cases, the administrator can also be expected to consider multiple objectives and/or constraints when maintaining and/or planning the distributed storage system. For example, the administrator might be asked to apply a recovery point objective (RPO), and/or an infrastructure spend budget constraint, and/or other parameters, while also determining the interdependent mix of multiple attributes of the distributed storage system that ensure capacity needs are met.
Unfortunately, legacy techniques for managing distributed storage systems exhibit severe limitations, at least in their ability to determine a distributed storage infrastructure plan that considers multiple objectives and/or constraints. As one example of such legacy system limitations, legacy distributed storage system management approaches often exhibit poor accuracy in predicting capacity requirements used by the administrator for planning. System management tools might not accurately capture certain observable periodicities and/or observable seasonalities in the forecasted demand—potentially resulting in overspending or underspending on infrastructure. In some cases, the legacy approaches fail to assess planning tasks at all, and/or fail to assess or present planning scenarios that consider planning parameters (e.g., constraints, objectives, etc.) that might be provided by the IT administrator and/or derived from the system observations (e.g., CPU performance, network bandwidth, etc.). For example, the administrator might want to test several “what if” planning scenarios by adjusting various parameters to determine a set of plans that predictably offer the best outcomes. Legacy approaches further fail to provide meaningful recommendations (e.g., schedule changes, purchase plans, remediations, etc.) to the administrator.
What is needed is a technique or techniques to improve over legacy and/or over other considered approaches. Some of the approaches described in this background section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.